Augmented facial animation

ABSTRACT

Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 16/987,707, filed Aug. 7, 2020, which is a continuation of U.S.patent application Ser. No. 16/172,664 (now U.S. Pat. No. 10,740,944),filed Oct. 26, 2018, which is a continuation of U.S. patent applicationSer. No. 15/822,271, filed Nov. 27, 2017, which is a continuation ofU.S. patent application Ser. No. 14/779,501 (now U.S. Pat. No.9,830,728), filed Sep. 23, 2015, which is a U.S. National StageApplication under 35 U.S.C. 371 from International ApplicationPCT/CN2014/094618, filed Dec. 23, 2014, all of which are herebyincorporated by reference in their entireties. Priority to U.S. patentapplication Ser. No. 16/987,707; U.S. patent application Ser. No.16/172,664; U.S. patent application Ser. No. 15/822,271; U.S. patentapplication Ser. No. 14/779,501; and International ApplicationPCT/CN2014/094618 is claimed

BACKGROUND

Users of mobile devices have access to text messaging, image messaging,video, and phone calling. But for those users, expressing emotions orfacial features in messages is difficult without using video.Additionally, users may be self-conscious and not want to be on video.An avatar to mimic the face of a user is used to simulate humanexpression of the user. However, avatars are not able to supportaugmented expressions.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 is a diagram illustrating a face with landmark points, accordingto an embodiment;

FIGS. 2A-2C are diagrams illustrating animated faces, according to anembodiment;

FIG. 3 is a flowchart illustrating a method for animating an avatarusing an alternative avatar expression, according to an embodiment;

FIG. 4 is a diagram illustrating a mobile device on which theconfigurations and techniques described herein may be deployed,according to an embodiment;

FIG. 5 is a block diagram of a machine upon which any one or more of thetechniques (e.g., methodologies) discussed herein may perform, accordingto an embodiment; and

FIG. 6 is a flowchart illustrating a method for animating an avatarusing emotion classification, according to an embodiment.

DETAILED DESCRIPTION

One way to display emotions or facial features on an avatar is to use anaugmented or alternative avatar expression, which is a nonhuman-likeexpression. An alternative avatar expression may be an exaggeratedexpression beyond the capabilities of a human to express on a face. Someexisting avatar applications may use icons or text bubbles toaccessorize an avatar, but do not show an alternative avatar expressionon the avatar. Further, the icons or text bubbles are triggered by abutton push or touch operation on a mobile device, which is disruptivefor the user. In an example, the alternative avatar expression describedbelow may be triggered by a sequence of facial features detected by amobile device. Facial emotion is a key ingredient in visualcommunication. In an example, a method of determining an emotionclassification for a facial expression may be used with an animatedavatar to display the emotion classification. The method of determiningthe emotion classification may include determining the emotionclassification and animating an avatar in response to determining theemotion classification while minimizing computational cost or time.

FIG. 1 is a diagram illustrating a face 100 with landmark points,according to an embodiment. The face 100 includes multiple landmarkpoints, including points on an ear 102, an eyebrow 104, an eye 106, anose 108, a mouth 110, 112, and 114, and a cheek 116. In an example, thelandmark points (e.g., 102, 104, 106, 108, 110, 112, or 114) may be usedto animate an avatar using an alternative avatar expression. Forexample, a specific movement pattern of a landmark, such as the eyebrow104 raising a specified distance, may be a sequence of facialexpressions. The sequence of facial expressions may be recognized anddetected as a specific movement pattern of the landmark, and thesequence of facial expressions may be mapped to an alternative avatarexpression. For example, a specific pattern of the eyebrow 104 raisingmay be mapped to eyes popping out (as shown in FIG. 2A). The trigger foranimating an alternative avatar expression may be solely due to thedetected sequence of facial expressions or movement of landmark points,or may use additional factors, such as a user indication to enter analternative avatar expression mode.

In an example, landmark points (e.g., 102, 104, 106, 108, 110, 112, or114) may be used to detect and classify emotions or expressions (e.g.,emotion classifications), such as a neutral, angry, disgusted, happy,surprised, or sad face. For example, surprise may be a detected emotionclassification using geometric information, such as a distance between apoint on an eye 106 and a point on an eyebrow 104. In another examplelandmark points such as forehead textual point 118, may be used todetermine an emotion classification. For example, using a density oflandmark points in an area of the face 100 between the eyebrowsincluding the forehead textual point 118, it may be determined that theface shows anger. In another example, both a distance between landmarkpoints and a density of landmark points in an area may be used todetermine an emotion classification. For example, determining an emotionclassification of disgust may include determining a distance between theeye point 106 and the eyebrow point 104 as well as a density of landmarkpoints around forehead textual point 118. Other examples may include arelative position of the landmark points on the mouth (e.g., 110, 112,and 114).

Determining an emotion classification of happiness or sadness mayinclude determining a distance between landmark points on the mouth,such as 112 and 114. The emotion classification of happiness or sadnessmay include a position or movement of a corner of the mouth 110.

FIGS. 2A-2C are diagrams illustrating animated faces 200A, 200B, and200C, according to an embodiment. FIGS. 2A-2C illustrate examples ofalternative avatar expressions. FIG. 2A includes a face 200Aillustrating an alternative avatar expression of eyes popping out. FIG.2B includes a face 200B illustrating an alternative avatar expression ofa jaw dropping. FIG. 2C includes a face 200C illustrating an alternativeavatar expression of stars over the face 200C and an “X” over each eye.Other alternative avatar expressions not shown include, but are notlimited to, a tongue unrolling out of a mouth, steam coming out of earsor face turning red, a neck or head spinning around (e.g., after beingpunched as in a cartoon), a nose growing, a vein popping out of aforehead, etc.

The alternative avatar expressions may be triggered by or mapped to asequence of facial expressions performed by the user, such as eyesrolling, an eyebrow lifting, ears twitching, a head or face shaking backand forth or nodding up and down, a nose flaring or twitching, a mouthopening or jaw lowering, a jaw moving side to side, showing teeth in amouth, snapping teeth, a tongue sticking out, rolling, or wagging, aneye winking, eyes blinking, or the like. The alternative avatarexpression may also be mapped to hand gestures or shoulder motions ofthe user. For example, a finger extending from a nose of a face may bemapped to an alternative avatar expression showing a nose growing, orshoulders shrugging may be mapped to an alternative avatar expressionshowing question marks or stars floating over a face. Other alternativeavatar expressions and other facial features may be used. In an example,a facial feature may be mapped to any one or more alternative avatarexpressions, or an alternative avatar expression may be mapped to anyone or more facial feature. In another example, a facial featureexpressed by a user may be mapped to an analogous alternative avatarexpression (e.g., raising eyebrows three times is mapped to eyes poppingout as in face 200A), although this is not necessary (e.g., the mappingmay be arbitrary).

In an example, an alternative avatar expression may be a special blendshape that may be integrated into existing avatar blend shapes foranimation. Interferences may exist between a special blend shape and anexisting avatar blend shape. The interference may be handled by using anadjustment to remove artifacts caused by the interference betweenadjacent or associated blend shapes.

FIG. 3 is a flowchart illustrating a method 300 for animating an avatarusing an alternative avatar expression, according to an embodiment. Inan example, the method 300 includes an operation 302 to capture a seriesof images of a face. The method 300 includes an operation 304 to detecta sequence of facial expressions of the face from the series of images.In an example, detecting the sequence of facial expressions may includedetecting the sequence of facial expressions during a specific timeframe, such as within a specified number of milliseconds, seconds,minutes, etc. In this manner, the method 300 ensures that random,sporadic, or inadvertent facial expressions over a long period are notinterpreted as a triggering sequence of facial expressions. The method300 may include resetting a timer if no sequence of facial expressionsis detected during the specific time frame. The timer may measure thespecific time frame, and if the sequence of facial expressions is tooslow and the timer expires before the sequence is complete, the sequencemay not be detected. In another example, detecting the sequence offacial expressions may include detecting movement of a specifiedlandmark point on the face. Detecting movement may include detectingrepeated movement, such as eyebrows raising multiple times, a headshaking back and forth, eyes blinking, a mouth opening repeatedly, etc.The repeated movement may be required to be done within the specifictime frame for the sequence to be detected. In an example, the landmarkpoint may be a tip of a nose, and when a face moves back and forth, thetip of the nose may be tracked as a landmark point. The movement of thetip of the nose may then be detected to include a sequence mapped to analternative avatar expression. In another example a trajectory of thelandmark may be detected, recognized, and may trigger an alternativeavatar expression. The trajectory may include a path traveled, a speed,a projected path, a projected speed, an acceleration, or the like.

A specified landmark point may include a user defined specific landmarkpoint. The detected movement may be done using dynamic time warping.Dynamic time warping is a technique for measuring similarities ordifferences between temporal sequences. Using dynamic time warping, themethod 300 may detect changes to landmarks on the face. The method 300may also use a support vector machine (SVM) to detect movement of thespecified landmark point.

The method 300 may include an operation 306 to determine an alternativeavatar expression mapped to the sequence of facial expressions.Determining the alternative avatar expression may include determiningthe alternative avatar expression using hand gestures or shouldermotions from the series of images. The hand gestures or shoulder motionsmay be present in the series of images and detected using techniquessimilar to those described above for detecting a sequence of facialexpressions. The method 300 may use a sequence of facial expressions, ahand gesture, a shoulder motion, or a combination to determine thealternative avatar expression.

In an example, the method 300 includes an operation 308 to animate anavatar using the alternative avatar expression. The method 300 mayinclude animating the avatar by mimicking the sequence of facialexpressions. For example, the method 300 may include animating separateparts of the avatar concurrently when mimicking the sequence of facialexpressions and using the alternative avatar expression. While use ofaugmented or alternative expressions is generally described, it isunderstood that more simplified animations of an avatar that mimic theuser's expressions may also be used in combination with augmentedexpressions. For example, when a user smiles, the corresponding useravatar may also smile in a non-augmented manner (e.g., mimicking theuser's facial expression). But when a user smiles and raises his or hereyebrows in a specific pattern (e.g., 3 times quickly), the avatar mayboth smile and have augmented eyes popping out from the avatar's face asan animation.

Mimicking the sequence of facial expressions may include animating aface of an avatar to smile and the alternative avatar expression mayinclude animating the face of the avatar to have the eyes of the avatarpop out. Both animations may be done concurrently so the avatar's facesmiles and the eyes pop out in overlapping time. The concurrentanimations may be separately created and displayed, although they mayappear to be the same animation. For the example above, the eyes poppingout may be a separate animation than the mimicked avatar smiling, and ifeyes on the face the animation are mimicking squint, the avatar mayignore that input when mimicking the face since the alternative avatarexpression is animating the eyes. The animations may also be donesequentially. In another example, animating the avatar may includeanimating the avatar in a blended animation when mimicking the sequenceof facial expressions and using the alternative avatar expression. Forexample, concurrent animation may include separate animation modules toanimate the alternative avatar expression and the mimicked animation anddisplay the animations together. In another example, blended animationmay include a combined animation module to animate the alternativeavatar expression and the mimicked animation together. With the blendedavatar in the eyes popping out and smiling avatar example above, whenthe eyes on the face squint, the blended avatar may have eyes poppingout that squint at the same time. Not all combinations for blending orconcurrent display of mimicking a face and displaying alternative avatarexpressions may be possible. The method 300 may include not displayingimpossible combinations.

The method 300 may rank displaying the alternative avatar expressionsover the mimicked expressions or vice versa, or the ranking may dependon the type of mimicked expression or the type of alternative avatarexpression. Animating the blended avatar may include smoothing outinterference in the blended animation by removing artifacts. The blendedavatar may include simple animation, such as icons (e.g., stars for adizzy expression as shown in FIG. 2C), or exaggerated facial actions,such as a movement of an avatar facial feature (e.g., jaw drop as shownin FIG. 2B).

In another example, the method 300 may include using an equation forblended animation. For example, an equation for an animation shape “B”may include:

$B = {B_{0} + {\sum\limits_{i = 0}^{n}B_{i}} + B_{\alpha}}$

Where B_(α) denotes the alternative avatar expression blend shape, B₀denotes a mean blend shape, and B_(i) denotes ordinary expression blendshapes. If B_(α) is independent of ordinary expression blend shapes,then there will be no interference. For example, an alternativeexpression blend shape for a nose extending may be independent ofordinary expression blend shapes. If B_(α) is not independent ofordinary expression blend shapes, then there will be an overlap andinterference. In an example, an adjustment may be introduced to theequation to remove artifacts or undesired geometry. For example, in FIG.2B, the geometry of the jaw may affect the geometry of a tongue, teeth,or muscles around the mouth. The adjustment may be used to correct thesegeometries. For example, an equation for the animation shape “B” mayinclude adjustment D_(α):

B=B ₀+Σ_(i=0) ^(n) B _(i) +B _(α) +D _(α)

In an example, the method 300 may include an alternative avatarexpression including a Graphics Interchange Format (GIF) image or avideo. The GIF image or video may be displayed concurrently with amimicked animation as described above. The GIF image or video may beoverlaid on the mimicked animation or on another alternative avatarexpression.

In another example, the method 300 may include an operation to receive auser indication selecting the alternative avatar expression. A user mayselect the alternative avatar expression from a list of possiblealternative avatar expressions or create one. The method 300 may includereceiving a user indication to enter an alternative avatar expressionmode before detecting the sequence of facial expressions of the face. Auser may turn the animation of the alternative avatar expression on oroff.

The method 300 may include determining an emotion classificationexpressed in a facial expression from a sequence of facial expressions.An emotion classification may include neutral, angry, disgusted, happy(smiling), surprised, or sad. The method 300 may include determining theemotion classification with minimal additional computational effort(e.g., power, time, heat, etc.). The method 300 may include estimatingpositions for a set of facial features, such as landmarks. The landmarkpositions may include positions of a corner, middle, or top of an eye,corner, middle, or top of a mouth, points on a nose, etc. In an example,the landmark positions may be estimated using regularized linearregressors. For example, independent variables set between zero and one.The regularized linear regressors may be used with a histogram of imagegradients for object detection. The object detection may detect facialfeatures that may be mapped to landmark points. In an example, themethod 300 may include generating a set of blendshape weights to animatean avatar. Blendshape weights of facial action units that are not usedin emotion classification may be determined separately from blendshapeweights of facial action units that are used in emotion classification.For example, landmark points may include a set of landmark points thatare not used in emotion classification and a set of landmark points thatare used in emotion classification. The sets of landmark points may bepredetermined or determined based on the emotion classification for ananimation.

Determining the emotion classification may include using a geometricdistance from a first landmark point to a second landmark point in thefacial expression. The geometry information may include a differencebetween the landmark positions of an estimated neutral expression of aface or facial feature and a current frame's landmark positions. Forexample, the geometry information may include a difference between aneyebrow landmark point in a neutral expression and the eyebrow landmarkpoint in a current expression. From the geometry information, an emotionclassification may be determined. To estimate the neutral expression ofthe current subject, several approaches may be used, such as medianfiltering, averaging, or a predetermined neutral classifier. Forexample, a neutral expression may include median values of a number offaces with a closed mouth.

Determining the emotion classification may include determining whether adensity of landmark points in an area of the face in the facialexpression exceeds a threshold. The textual information may include aconcatenation of an image feature of a neutral expression and an imagefeature of a current frame. Textual information may include determininga density of landmark points in an area of the face in the facialexpression and determining whether that density exceeds a predeterminedthreshold. The threshold may include a range (e.g., above a firstpredetermined threshold may indicate anger and above a secondpredetermined threshold may indicate disgust where anger would includethe range above the first predetermined threshold and below the secondpredetermined threshold). The method 300 may include using the sameimage descriptor to detect facial landmarks and textural variations.This may use negligible or very little additional computation cost(e.g., can be done quickly, efficiently, with fewer cycles than othermethods, with less heat, etc.) for emotion classification. The output ofemotion classification may be a set of probabilities for emotioncategories (e.g., six standard emotion categories of neutral, angry,disgust, happy, surprise, and sad).

In an example, both the geometric distance and the density of landmarkpoints in the area of the face may be used to determine the emotionclassification. In another example, a model may be used to determine theemotion classification using facial expressions with known emotionclassifications, such as predetermined facial expressions, facialexpressions stored in a database, or modeled facial expressions. Themodeled facial expressions may include a model between an input imageand an emotional intensity. The method 300 may include determining anemotional intensity of a facial expression and using the intensity toanimate an avatar. Determining an emotion classification may includedetermining the emotion classification semantically, such as bydetermining the classification of an emotion rather than solely onfacial expressions. Determining the emotion classification may beefficient compared to deciphering emotions using facial expressions. Inan example, the method 300 may include determining the alternativeavatar expression using the emotion classification. In an example, bothgeometry information (fg) and textural information (fi) may be usedtogether to determine an emotion classification. For example, a finalfeature may include a concatenation of fg and fi, (fg,fi). In anexample, a simple support vector machine (SVM) with several thousands oftraining examples may be used. In another example, any kind of machinelearning technique may be used for making an emotion classification.

In an example, animating the avatar may include animating the avatarusing the emotion classification. In an example, animating the avatarmay include using dynamic weight adjustment. The method 300 may includecomputing an emotion intensity for an emotion classification (orprobabilities of an emotion classification). An emotion intensity may beused as a blending weight of the corresponding emotional blendshapes ofan avatar. There may be a conflict between a blendshape weight from afacial mesh tracker and a blendshape weight from an emotionclassification. If there is a conflict, the blendshape weights may bedynamically renormalized according to the emotion intensity. Forexamples, in surprise face, the tracker may increase the weight of amouth openness blendshape and the emotion classifier may increase theweight of a surprise blendshape. Simple addition of these blendshapesmay result in an unnatural mouth openness. This issue may be correctedby dynamically adjusting the emotion blendshape weight to have the samemouth openness from as the tracker weight of the mouth opennessblendshape. Other conflicting blendshapes may also be corrected usingthis technique. In an example, an alternative avatar expression may bedetermined using the emotion classification or the emotion intensity orboth. For example, the alternative avatar expression may include steamcoming from the avatar's ears when the emotion classification is anger.

In another example, animating the avatar may include generating andusing canned animation. An emotion intensity may be used to trigger acanned animation. A canned animation may include any predeterminedanimation, such as a GIF image, a video, a prerecorded animation of aspecified avatar, an alternative avatar expression, or the like. Thecanned animation may include using the alternative avatar expression toanimate an avatar. The alternative avatar expression may be determinedusing the emotion classification or the emotion intensity or both. Forexample, the alternative avatar expression may include steam coming fromthe avatar's ears when the emotion classification is anger. An emotionintensity may be aggregated over several frames. The aggregated emotionintensity may be used to trigger a canned animation. For example, if anemotion intensity remains high for some, a majority, or all of theseveral frames, the canned animation may be triggered.

FIG. 4 is a diagram illustrating a mobile device 400 on which theconfigurations and techniques described herein may be deployed,according to an embodiment. FIG. 4 provides an example illustration of amobile device 400, such as a user equipment (UE), a mobile station (MS),a mobile wireless device, a mobile communication device, a tablet, ahandset, or other type of mobile wireless computing device. The mobiledevice 400 may include one or more antennas 408 within housing 402 thatare configured to communicate with a hotspot, base station (BS), an eNB,or other type of WLAN or WWAN access point. The mobile device may beconfigured to communicate using multiple wireless communicationstandards, including standards selected from 3GPP LTE, WiMAX, High SpeedPacket Access (HSPA), Bluetooth, and Wi-Fi standard definitions. Themobile device 400 may communicate using separate antennas for eachwireless communication standard or shared antennas for multiple wirelesscommunication standards. The mobile device 400 may communicate in aWLAN, a WPAN, and/or a WWAN.

FIG. 4 also provides an illustration of a microphone 420 and one or morespeakers 412 that may be used for audio input and output from the mobiledevice 400. A display screen 404 may be a liquid crystal display (LCD)screen, or other type of display screen such as an organic lightemitting diode (OLED) display. The display screen 404 may be configuredas a touch screen. The touch screen may use capacitive, resistive, oranother type of touch screen technology. An application processor 414and a graphics processor may be coupled to internal memory 416 toprovide processing and display capabilities. A non-volatile memory port410 may also be used to provide data input/output options to a user. Thenon-volatile memory port 410 may also be used to expand the memorycapabilities of the mobile device 400. A keyboard 406 may be integratedwith the mobile device 400 or wirelessly connected to the mobile device400 to provide additional user input. A virtual keyboard may also beprovided using the touch screen. A camera 422 located on the front(display screen) side or the rear side of the mobile device 400 may alsobe integrated into the housing 402 of the mobile device 400.

The mobile device 400 may include a facial recognition module 424, aprocessing module 426, or an animation module 418. In an example, themobile device 400 may include an image capture device, such as thecamera 422. The image capture device may capture a series of images of aface.

The facial recognition module 424 may detect a sequence of facialexpressions of the face from the series of images. In an example, thefacial recognition module 424 may detect the sequence of facialexpressions during a specific time frame, such as over a period ofmilliseconds, seconds, minutes, etc. For example, the facial recognitionmodule 424 may detect if the sequence of facial expressions is completedbefore a specific amount expires. The facial recognition module 424 maydetect movement of a specified landmark point on the face to detect thesequence of facial expressions. The movement may be a repeated movement,such as raising eyebrows three, four, or five times, blinking twice,shaking head repeatedly, etc. To detect movement of the specifiedlandmark point, the facial recognition module 424 may use dynamic timewarping as described above. The facial recognition module 424 maycommence detecting the sequence after an indication to enter analternative avatar expression mode is received (by the facialrecognition module 424, the processing module 426, or the like). Theindication may be a user indication.

The processing module 426 may determine an alternative avatar expressionmapped to the sequence of facial expressions. The processing module 426may use hand gestures or shoulder motions, such as from the series ofimages, to determine the alternative avatar expression. The animationmodule 418 may animate an avatar using the alternative avatarexpression. In an example, the animation module 418 may animate theavatar by mimicking the sequence of facial expressions. The animationmodule 418 may animate separate parts of the avatar concurrently whenmimicking the sequence of facial expressions and using the alternativeavatar expression. For example, mimicking the sequence of facialexpressions may result in an avatar animation that may animate the faceof the avatar to smile and the alternative avatar expression may animatethe face of the avatar to have the eyes of the avatar pop out. In thisexample, both animations may be done concurrently so the avatar's facesmiles and the eyes pop out. The animation module 418 may animate theavatar in a blended animation when mimicking the sequence of facialexpressions and using the alternative avatar expression. The animationmodule 418 may smooth out interference in the blended animation byremoving artifacts.

In another example, the alternative avatar expression may be overlaid onthe mimicked animation using a series of images, such as a GraphicsInterchange Format (GIF) image or a video. The overlay may be doneconcurrently with the mimicked animation. In other examples, thealternative avatar expression may cause the avatar to stop beinganimated with the mimicked animation and only display the alternativeavatar expression. The alternative avatar expression may be a userdefined alternative avatar expression. A user may also define thesequence of facial expressions that is mapped to an alternative avatarexpression.

In an example, the processing module 426 may determine an emotionclassification expressed in a facial expression from the sequence offacial expressions. Emotion classification may include using geometricinformation of facial features or textural information around thosefeature positions. In an example, facial emotions may be classified intodifferent emotional groups, such as neutral, anger, disgust, happiness,surprise, or sadness. Some of them, for example, surprise, may beestimated using geometric information such as distance between eye andeyebrow, and some of them, for example, anger, may be estimated usingtextural information such as wrinkles between eyebrows, etc. Otheremotion classifications may be estimated using both geometry and textureinformation. For example, geometric information of facial features andtextural variations around facial features may be combined into a singleframework to increase the accuracy of emotion classification.

The processing module 426 may determine the emotion classification usinga geometric distance from a first landmark point to a second landmarkpoint in the facial expression. The geometry information may include adifference between the first landmark point and the second landmarkpoint. In an example, the first landmark point may include an estimateof a landmark position for a neutral expression of the face and thesecond landmark point may include a current frame's landmark point. Toestimate the neutral expression of the face, the processing module 426may use several approaches such as median filtering, averaging,dedicated neutral classifier, or the like. In an example, a neutralexpression may include a median value of all faces with a closed mouth.

The processing module 426 may determine the emotion classification bydetermining whether a density of landmark points in an area of the facein the facial expression exceeds a threshold. The density of landmarkpoints in an area of the face may include textual information, such as adensity of landmark points between a set of eyebrows. In anotherexample, the processing module 426 may determine the emotionclassification using a geometric distance and using a density oflandmark points in an area of a face.

In an example, the processing module 426 may determine the alternativeavatar expression using the emotion classification. The alternativeavatar expression may be mapped to the emotion classification. Forexample, the emotion classification may be surprise and the alternativeavatar expression may be similar to the jaw drop of FIG. 2B. In anotherexample, an alternative avatar expression may be mapped to an emotionclassification as defined by a user indication.

The animation module 418 may animate the avatar using the emotionclassification. For example, the animation module 418 may use dynamicweight adjustment to animate the avatar. In an example, an emotionintensity (or probability) may be determined or estimated. The emotionintensity may be used as a blending weight for the corresponding emotionblendshape of an avatar. There may be conflicts between a blendshapeweight from a facial mesh tracker and a blendshape weight from anemotion classification. The conflict may be corrected by dynamicallyrenormalizing the weights according to an emotion intensity. Forexample, in a surprise face, the tracker may increase the weight of amouth openness blendshape and the emotion classifier may increase theweight of a surprise blendshape. Addition of these blendshapes mayresult in an unnatural mouth openness. This issue may be corrected bydynamically adjusting an emotion blendshape weight to have the samemouth openness from the tracker. In another example, the animationmodule 418 may use a canned animation to animate the avatar. An emotionintensity may be used to trigger a canned animation. This usage mayinclude the GIF animation described above for the alternative avatarexpression. Emotion intensity may be aggregated over several frames andthe canned animation may be triggered when an emotion intensity ispresent for some, a majority, or all of the several frames.

FIG. 5 is a block diagram of a machine 500 upon which any one or more ofthe techniques (e.g., methodologies) discussed herein may perform,according to an embodiment. In alternative embodiments, the machine 500may operate as a standalone device or may be connected (e.g., networked)to other machines. In a networked deployment, the machine 500 mayoperate in the capacity of a server machine, a client machine, or bothin server-client network environments. In an example, the machine 500may act as a peer machine in peer-to-peer (P2P) (or other distributed)network environment. The machine 500 may be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), amobile telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein, such as cloudcomputing, software as a service (SaaS), other computer clusterconfigurations.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operationswhen operating. A module includes hardware. In an example, the hardwaremay be specifically configured to carry out a specific operation (e.g.,hardwired). In an example, the hardware may include configurableexecution units (e.g., transistors, circuits, etc.) and a computerreadable medium containing instructions, where the instructionsconfigure the execution units to carry out a specific operation when inoperation. The configuring may occur under the direction of theexecutions units or a loading mechanism. Accordingly, the executionunits are communicatively coupled to the computer readable medium whenthe device is operating. In this example, the execution units may be amember of more than one module. For example, under operation, theexecution units may be configured by a first set of instructions toimplement a first module at one point in time and reconfigured by asecond set of instructions to implement a second module.

Machine (e.g., computer system) 500 may include a hardware processor 502(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 504 and a static memory 506, some or all of which may communicatewith each other via an interlink (e.g., bus) 508. The machine 500 mayfurther include a display unit 510, an alphanumeric input device 512(e.g., a keyboard), and a user interface (UI) navigation device 514(e.g., a mouse). In an example, the display unit 510, alphanumeric inputdevice 512 and UI navigation device 514 may be a touch screen display.The machine 500 may additionally include a storage device (e.g., driveunit) 516, a signal generation device 518 (e.g., a speaker), a networkinterface device 520, and one or more sensors 521, such as a globalpositioning system (GPS) sensor, compass, accelerometer, or othersensor. The machine 500 may include an output controller 528, such as aserial (e.g., universal serial bus (USB), parallel, or other wired orwireless (e.g., infrared (IR), near field communication (NFC), etc.)connection to communicate or control one or more peripheral devices(e.g., a printer, card reader, etc.).

The storage device 516 may include a machine readable medium 522 that isnon-transitory on which is stored one or more sets of data structures orinstructions 524 (e.g., software) embodying or utilized by any one ormore of the techniques or functions described herein. The instructions524 may also reside, completely or at least partially, within the mainmemory 504, within static memory 506, or within the hardware processor502 during execution thereof by the machine 500. In an example, one orany combination of the hardware processor 502, the main memory 504, thestatic memory 506, or the storage device 516 may constitute machinereadable media.

While the machine readable medium 522 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 524.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 500 and that cause the machine 500 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having invariant (e.g., rest) mass.Accordingly, massed machine-readable media are not transitorypropagating signals. Specific examples of massed machine readable mediamay include: non-volatile memory, such as semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theinstructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium via the networkinterface device 520 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 520 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 526. In an example, the network interfacedevice 520 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 500, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

FIG. 6 is a flowchart illustrating a method 600 for animating an avatarusing emotion classification, according to an embodiment. In an example,the method 600 may be combined with aspects of the method 300 describedabove. In another example, any aspects of the method 300 described abovemay be done independently of method 300, such as using method 600. Themethod 600 may include an operation 602 to detect one or more faciallandmarks. In an example, geometric information of facial features andtextural variations around facial features may be used and combined intoa single framework to increase the accuracy of emotion classification.FIG. 6 shows a method that describes an example of an overall flowchartof emotional classification and its usage on avatars in visualcommunication. The method 600 may include an operation 604 to generateblendshape weights. The method 600 may include an operation 606 todetermine emotion classification for a facial expression. In an example,the method 600 includes an operation 608 to do dynamic weight adjustmentof the blendshape weight using the emotion classification. In anotherexample, the method 600 includes an operation 610 to do canned animationgeneration using the emotion classification.

Various Notes & Examples

Each of these non-limiting examples may stand on its own, or may becombined in various permutations or combinations with one or more of theother examples.

Example 1 includes the subject matter embodied by a system for mappingfacial expressions to an alternative avatar expression comprising: animage capture device to capture a series of images of a face, a facialrecognition module to detect a sequence of facial expressions of theface from the series of images, a processing module to determine thealternative avatar expression mapped to the sequence of facialexpressions, and an animation module to animate an avatar using thealternative avatar expression.

In Example 2, the subject matter of Example 1 can optionally includewherein to detect the sequence of facial expressions, the facialrecognition module is to detect the sequence of facial expressionsduring a specific time frame.

In Example 3, the subject matter of one or any combination of Examples1-2 can optionally include wherein to determine the alternative avatarexpression, the processing module is to use hand gestures from theseries of images.

In Example 4, the subject matter of one or any combination of Examples1-3 can optionally include wherein to determine the alternative avatarexpression, the processing module is to use shoulder motions from theseries of images.

In Example 5, the subject matter of one or any combination of Examples1-4 can optionally include wherein to detect the sequence of facialexpressions, the facial recognition module is to detect movement of aspecified landmark point on the face.

In Example 6, the subject matter of one or any combination of Examples1-5 can optionally include wherein to detect movement of the specifiedlandmark point on the face, the facial recognition module is to detectrepeated movement.

In Example 7, the subject matter of one or any combination of Examples1-6 can optionally include wherein to detect movement of the specifiedlandmark point on the face, the facial recognition module is to usedynamic time warping.

In Example 8, the subject matter of one or any combination of Examples1-7 can optionally include wherein the alternative avatar expression isa user defined alternative avatar expression.

In Example 9, the subject matter of one or any combination of Examples1-8 can optionally include wherein the animation module is to animatethe avatar by mimicking the sequence of facial expressions.

In Example 10, the subject matter of one or any combination of Examples1-9 can optionally include wherein the animation module is to animateseparate parts of the avatar concurrently when mimicking the sequence offacial expressions and using the alternative avatar expression.

In Example 11, the subject matter of one or any combination of Examples1-10 can optionally include wherein the alternative avatar expressionincludes a Graphics Interchange Format (GIF) image.

In Example 12, the subject matter of one or any combination of Examples1-11 can optionally include wherein the animation module is to animatethe avatar in a blended animation when mimicking the sequence of facialexpressions and using the alternative avatar expression.

In Example 13, the subject matter of one or any combination of Examples1-12 can optionally include wherein the animation module is to smoothout interference in the blended animation by removing artifacts.

In Example 14, the subject matter of one or any combination of Examples1-13 can optionally include wherein the facial recognition module is todetect the sequence of facial expressions of the face after receiving auser indication to enter an alternative avatar expression mode.

In Example 15, the subject matter of one or any combination of Examples1-14 can optionally include wherein the processing module is todetermine an emotion classification expressed in a facial expressionfrom the sequence of facial expressions.

In Example 16, the subject matter of one or any combination of Examples1-15 can optionally include wherein to determine the emotionclassification, the processing module is to determine the emotionclassification using a geometric distance from a first landmark point toa second landmark point in the facial expression.

In Example 17, the subject matter of one or any combination of Examples1-16 can optionally include wherein to determine the emotionclassification, the processing module is to determine whether a densityof landmark points in an area of the face in the facial expressionexceeds a threshold.

In Example 18, the subject matter of one or any combination of Examples1-17 can optionally include wherein to determine the alternative avatarexpression, the processing module is to determine the alternative avatarexpression using the emotion classification.

In Example 19, the subject matter of one or any combination of Examples1-18 can optionally include wherein to animate the avatar, the animationmodule is to animate the avatar using the emotion classification.

Example 20 includes the subject matter embodied by a method for mappingfacial expressions to an alternative avatar expression comprising:capturing a series of images of a face, detecting a sequence of facialexpressions of the face from the series of images, determining thealternative avatar expression mapped to the sequence of facialexpressions, and animating an avatar using the alternative avatarexpression.

In Example 21, the subject matter of Example 20 can optionally includewherein detecting the sequence of facial expressions includes detectingthe sequence of facial expressions during a specific time frame.

In Example 22, the subject matter of one or any combination of Examples20-21 can optionally include wherein determining the alternative avatarexpression includes determining the alternative avatar expression usinghand gestures from the series of images.

In Example 23, the subject matter of one or any combination of Examples20-22 can optionally include wherein determining the alternative avatarexpression includes determining the alternative avatar expression usingshoulder motions from the series of images.

In Example 24, the subject matter of one or any combination of Examples20-23 can optionally include wherein detecting the sequence of facialexpressions includes detecting movement of a specified landmark point onthe face.

In Example 25, the subject matter of one or any combination of Examples20-24 can optionally include wherein detecting movement of the specifiedlandmark point on the face includes detecting repeated movement.

In Example 26, the subject matter of one or any combination of Examples20-25 can optionally include wherein detecting movement of the specifiedlandmark point on the face includes using dynamic time warping.

In Example 27, the subject matter of one or any combination of Examples20-26 can optionally include further comprising receiving a userindication selecting the alternative avatar expression.

In Example 28, the subject matter of one or any combination of Examples20-27 can optionally include further comprising animating the avatar bymimicking the sequence of facial expressions.

In Example 29, the subject matter of one or any combination of Examples20-28 can optionally include further comprising animating separate partsof the avatar concurrently when mimicking the sequence of facialexpressions and using the alternative avatar expression.

In Example 30, the subject matter of one or any combination of Examples20-29 can optionally include wherein animating the avatar includesanimating the avatar in a blended animation when mimicking the sequenceof facial expressions and using the alternative avatar expression.

In Example 31, the subject matter of one or any combination of Examples20-30 can optionally include further comprising smoothing outinterference in the blended animation by removing artifacts.

In Example 32, the subject matter of one or any combination of Examples20-31 can optionally include wherein the alternative avatar expressionincludes a Graphics Interchange Format (GIF) image.

In Example 33, the subject matter of one or any combination of Examples20-32 can optionally include further comprising receiving a userindication to enter an alternative avatar expression mode beforedetecting the sequence of facial expressions of the face.

In Example 34, the subject matter of one or any combination of Examples20-33 can optionally include further comprising determining an emotionclassification expressed in a facial expression from the sequence offacial expressions.

In Example 35, the subject matter of one or any combination of Examples20-34 can optionally include wherein determining the emotionclassification includes determining the emotion classification using ageometric distance from a first landmark point to a second landmarkpoint in the facial expression.

In Example 36, the subject matter of one or any combination of Examples20-35 can optionally include wherein determining the emotionclassification includes determining whether a density of landmark pointsin an area of the face in the facial expression exceeds a threshold.

In Example 37, the subject matter of one or any combination of Examples20-36 can optionally include wherein determining the alternative avatarexpression includes determining the alternative avatar expression usingthe emotion classification.

In Example 38, the subject matter of one or any combination of Examples20-37 can optionally include wherein animating the avatar includesanimating the avatar using the emotion classification.

Example 39 includes at least one machine-readable medium includinginstructions for receiving information, which when executed by amachine, cause the machine to perform any of the methods of Examples20-38.

Example 40 includes an apparatus comprising means for performing any ofthe methods of Examples 20-38.

Example 41 includes the subject matter embodied by an apparatuscomprising: means for capturing a series of images of a face, means fordetecting a sequence of facial expressions of the face from the seriesof images, means for determining an alternative avatar expression mappedto the sequence of facial expressions, and means for animating an avatarusing the alternative avatar expression.

In Example 42, the subject matter of Example 41 can optionally includewherein the means for detecting the sequence of facial expressionsinclude means for detecting the sequence of facial expressions during aspecific time frame.

In Example 43, the subject matter of one or any combination of Examples41-42 can optionally include wherein the means for determining thealternative avatar expression include means for determining thealternative avatar expression using hand gestures from the series ofimages.

In Example 44, the subject matter of one or any combination of Examples41-43 can optionally include wherein the means for determining thealternative avatar expression include means for determining thealternative avatar expression using shoulder motions from the series ofimages.

In Example 45, the subject matter of one or any combination of Examples41-44 can optionally include wherein the means for detecting thesequence of facial expressions include means for detecting movement of aspecified landmark point on the face.

In Example 46, the subject matter of one or any combination of Examples41-45 can optionally include wherein the means for detecting movement ofthe specified landmark point on the face include means for detectingrepeated movement.

In Example 47, the subject matter of one or any combination of Examples41-46 can optionally include wherein the means for detecting movement ofthe specified landmark point on the face include means for using dynamictime warping.

In Example 48, the subject matter of one or any combination of Examples41-47 can optionally include further comprising means for receiving auser indication selecting the alternative avatar expression.

In Example 49, the subject matter of one or any combination of Examples41-48 can optionally include further comprising means for animating theavatar by mimicking the sequence of facial expressions.

In Example 50, the subject matter of one or any combination of Examples41-49 can optionally include further comprising means for animatingseparate parts of the avatar concurrently when mimicking the sequence offacial expressions and using the alternative avatar expression.

In Example 51, the subject matter of one or any combination of Examples41-50 can optionally include wherein the means for animating the avatarinclude means for animating the avatar in a blended animation whenmimicking the sequence of facial expressions and using the alternativeavatar expression.

In Example 52, the subject matter of one or any combination of Examples41-51 can optionally include further comprising means for smoothing outinterference in the blended animation by removing artifacts.

In Example 53, the subject matter of one or any combination of Examples41-52 can optionally include wherein the alternative avatar expressionincludes a Graphics Interchange Format (GIF) image.

In Example 54, the subject matter of one or any combination of Examples41-53 can optionally include further comprising means for receiving auser indication to enter an alternative avatar expression mode beforedetecting the sequence of facial expressions of the face.

In Example 55, the subject matter of one or any combination of Examples41-54 can optionally include further comprising means for determining anemotion classification expressed in a facial expression from thesequence of facial expressions.

In Example 56, the subject matter of one or any combination of Examples41-55 can optionally include wherein the means for determining theemotion classification include means for determining the emotionclassification using a geometric distance from a first landmark point toa second landmark point in the facial expression.

In Example 57, the subject matter of one or any combination of Examples41-56 can optionally include wherein the means for determining theemotion classification include means for determining whether a densityof landmark points in an area of the face in the facial expressionexceeds a threshold.

In Example 58, the subject matter of one or any combination of Examples41-57 can optionally include wherein the means for determining thealternative avatar expression include means for determining thealternative avatar expression using the emotion classification.

In Example 59, the subject matter of one or any combination of Examples41-58 can optionally include wherein the means for animating the avatarinclude means for animating the avatar using the emotion classification.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention may be practiced. These embodiments are also referred toherein as “examples.” Such examples may include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods may include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments may be combined with each other in various combinations orpermutations.

1. (canceled)
 2. At least one storage device comprising instructionsthat, when executed, cause processor circuitry to: identify an augmentedfacial appearance based on user input, the augmented facial appearanceto be presented on a display based on one or more images of a facecaptured by a camera, the augmented facial appearance corresponding toan expression beyond an ability of a human to express; detect aparticular movement of the face in successive ones of the one or moreimages; and in response to detection of the particular movement,generate augmented images based on the successive images, the augmentedimages including the augmented facial appearance.
 3. The at least onestorage device of claim 2, wherein the particular movement includes amouth opening.
 4. The at least one storage device of claim 2, whereinthe augmented facial appearance includes a mouth that is larger thanhumanly possible relative to other facial features in the augmentedfacial appearance.
 5. The at least one storage device of claim 2,wherein the instructions cause the processor circuitry to cause thecamera to capture the one or more images of the face.
 6. The at leastone storage device of claim 2, wherein the instructions cause theprocessor circuitry to detect a hand in the one or more images.
 7. Theat least one storage device of claim 2, wherein the instructions causethe processor circuitry to detect the particular movement based ondetection of movement of a particular landmark point on the face.
 8. Theat least one storage device of claim 7, wherein the instructions causethe processor circuitry to detect the movement of the particularlandmark point using dynamic time warping.
 9. The at least one storagedevice of claim 2, wherein the augmented facial appearance is one of aplurality of augmented facial appearances, different ones of theplurality of augmented facial appearances selectable by a user.
 10. Amethod comprising: identifying an augmented facial animation based onuser input, the augmented facial animation to be presented on a displaybased on one or more images of a face captured by a camera, theaugmented facial animation including an appearance beyond an ability ofa human face to replicate; detecting a particular movement of the facein successive ones of the one or more images; and in response todetection of the particular movement, generating, by executing aninstruction with processor circuitry, augmented images based on thesuccessive images, the augmented images including the augmented facialanimation.
 11. The method of claim 10, wherein the particular movementincludes a mouth opening.
 12. The method of claim 10, wherein theaugmented facial animation includes a mouth that is larger than humanlypossible relative to other facial features in the augmented facialanimation.
 13. The method of claim 10, further including causing thecamera to capture the one or more images of the face.
 14. The method ofclaim 10, further including detecting a hand in the one or more images.15. The method of claim 10, further including detecting the particularmovement based on detection of movement of a particular landmark pointon the face.
 16. The method of claim 15, further including detecting themovement of the particular landmark point using dynamic time warping.17. The method of claim 10, wherein the augmented facial animation isone of a plurality of augmented facial animations, different ones of theplurality of augmented facial animations selectable by a user.
 18. Atleast one storage device comprising instructions that, when executed,cause processor circuitry to: identify a facial feature to be changedbased on user input, the changed facial feature to be presented on adisplay based on one or more images of a face captured by a camera, thefacial feature to be changed to produce a surreal facial appearance;detect a particular movement of the face in successive ones of the oneor more images; and in response to detection of the particular movement,generate updated images based on the successive images, the updatedimages including the surreal facial appearance.
 19. The at least onestorage device of claim 18, wherein the particular movement includes amouth opening.
 20. The at least one storage device of claim 18, whereinthe surreal facial appearance corresponds to a mouth that is larger thanhumanly possible relative to other facial features.
 21. The at least onestorage device of claim 18, wherein the processor circuitry is to causethe camera to capture the one or more images of the face.
 22. The atleast one storage device of claim 18, wherein the processor circuitry isto detect a hand in the one or more images.
 23. The at least one storagedevice of claim 18, wherein the processor circuitry is to detect theparticular movement based on detection of movement of a particularlandmark point on the face.
 24. The at least one storage device of claim23, wherein the processor circuitry is to detect the movement of theparticular landmark point using dynamic time warping.
 25. The at leastone storage device of claim 18, wherein the facial feature to be changedis one of a plurality of facial features to be changed, different onesof the plurality of facial features selectable by a user.
 26. Anapparatus comprising memory; instructions; and processor circuitry toexecute the instructions to: identify an augmented facial image based onuser input, the augmented facial image to be presented on a displaybased on one or more images of a face captured by a camera, theaugmented facial image corresponding to a surreal facial appearance;detect a particular movement of the face in successive ones of the oneor more images; and in response to detection of the particular movement,generate updated images based on the successive images, the updatedimages including the surreal facial appearance.
 27. The apparatus ofclaim 26, wherein the particular movement includes a mouth opening. 28.The apparatus of claim 26, wherein the surreal facial appearancecorresponds to a mouth that is larger than humanly possible relative toother facial features.
 29. The apparatus of claim 26, wherein theprocessor circuitry is to cause the camera to capture the one or moreimages of the face.
 30. The apparatus of claim 26, wherein the processorcircuitry is to detect a hand in the one or more images.
 31. Theapparatus of claim 26, wherein the processor circuitry is to detect theparticular movement based on detection of movement of a particularlandmark point on the face.
 32. The apparatus of claim 31, wherein theprocessor circuitry is to detect the movement of the particular landmarkpoint using dynamic time warping.
 33. The apparatus of claim 26, whereinthe surreal facial appearance is one of a plurality of surreal facialappearances, different ones of the plurality of surreal facialappearances selectable by a user.